
from __future__ import annotations

import unittest

from transformers import AutoTokenizer, MBartConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
							import tensorflow as tf

							from transformers import TFAutoModelForSeqaSeqLM, TFMBartForConditionalGeneration, TFMBartModel

@require_tf
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			MBartConfig
			__UpperCamelCase   :    str          =			{}
			__UpperCamelCase   :    int          =			"""gelu"""

			def __init__(							self			:							Any    ,						snake_case_			:							Optional[int]    ,						snake_case_			:							int=13    ,						snake_case_			:							str=7    ,						snake_case_			:							Any=True    ,						snake_case_			:							List[Any]=False    ,						snake_case_			:							Optional[int]=99    ,						snake_case_			:							int=32    ,						snake_case_			:							Tuple=2    ,						snake_case_			:							Optional[int]=4    ,						snake_case_			:							int=37    ,						snake_case_			:							Union[str, Any]=0.1    ,						snake_case_			:							int=0.1    ,						snake_case_			:							str=20    ,						snake_case_			:							str=2    ,						snake_case_			:							List[str]=1    ,						snake_case_			:							List[Any]=0    ,						):
							UpperCamelCase_:				str						=    parent
							UpperCamelCase_:				Union[str, Any]						=    batch_size
							UpperCamelCase_:				Optional[int]						=    seq_length
							UpperCamelCase_:				Optional[Any]						=    is_training
							UpperCamelCase_:				List[Any]						=    use_labels
							UpperCamelCase_:				int						=    vocab_size
							UpperCamelCase_:				List[str]						=    hidden_size
							UpperCamelCase_:				int						=    num_hidden_layers
							UpperCamelCase_:				Dict						=    num_attention_heads
							UpperCamelCase_:				Dict						=    intermediate_size
							UpperCamelCase_:				List[Any]						=    hidden_dropout_prob
							UpperCamelCase_:				Optional[Any]						=    attention_probs_dropout_prob
							UpperCamelCase_:				Any						=    max_position_embeddings
							UpperCamelCase_:				Tuple						=    eos_token_id
							UpperCamelCase_:				Any						=    pad_token_id
							UpperCamelCase_:				Dict						=    bos_token_id

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							UpperCamelCase_:				List[str]						=    ids_tensor([self.batch_size, self.seq_length - 1]    ,						self.vocab_size   )
							UpperCamelCase_:				Union[str, Any]						=    tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size   )    ,						1   )
							UpperCamelCase_:				Dict						=    tf.concat([input_ids, eos_tensor]    ,						axis=1   )

							UpperCamelCase_:				Tuple						=    ids_tensor([self.batch_size, self.seq_length]    ,						self.vocab_size   )

							UpperCamelCase_:				Optional[int]						=    self.config_cls(
							    vocab_size=self.vocab_size    ,						d_model=self.hidden_size    ,						encoder_layers=self.num_hidden_layers    ,						decoder_layers=self.num_hidden_layers    ,						encoder_attention_heads=self.num_attention_heads    ,						decoder_attention_heads=self.num_attention_heads    ,						encoder_ffn_dim=self.intermediate_size    ,						decoder_ffn_dim=self.intermediate_size    ,						dropout=self.hidden_dropout_prob    ,						attention_dropout=self.attention_probs_dropout_prob    ,						max_position_embeddings=self.max_position_embeddings    ,						eos_token_ids=[2]    ,						bos_token_id=self.bos_token_id    ,						pad_token_id=self.pad_token_id    ,						decoder_start_token_id=self.pad_token_id    ,						**self.config_updates    ,						)
							UpperCamelCase_:				Any						=    prepare_mbart_inputs_dict(snake_case_    ,						snake_case_    ,						snake_case_   )
							return config, inputs_dict




			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							List[str]    ,						snake_case_			:							List[str]   ):
							UpperCamelCase_:				List[str]						=    TFMBartModel(config=snake_case_   ).get_decoder()
							UpperCamelCase_:				List[Any]						=    inputs_dict["""input_ids"""]

							UpperCamelCase_:				Optional[Any]						=    input_ids[:1, :]
							UpperCamelCase_:				List[str]						=    inputs_dict["""attention_mask"""][:1, :]
							UpperCamelCase_:				Optional[int]						=    inputs_dict["""head_mask"""]
							UpperCamelCase_:				str						=    1

							# first forward pass
							UpperCamelCase_:				Tuple						=    model(snake_case_    ,						attention_mask=snake_case_    ,						head_mask=snake_case_    ,						use_cache=snake_case_   )

							UpperCamelCase_, UpperCamelCase_:				str						=    outputs.to_tuple()
							UpperCamelCase_:				List[Any]						=    past_key_values[1]







def 							A__       (    lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase  ,     lowerCamelCase=None  ,     lowerCamelCase=None  ,     lowerCamelCase=None  ,     lowerCamelCase=None  ,     lowerCamelCase=None  ,     )	->						Union[str, Any]:
				if attention_mask is None:
								UpperCamelCase_:				str						=    tf.cast(tf.math.not_equal(lowerCamelCase  ,     config.pad_token_id    )  ,     tf.inta    )
				if decoder_attention_mask is None:
								UpperCamelCase_:				int						=    tf.concat(
								    [
								        tf.ones(decoder_input_ids[:, :1].shape  ,     dtype=tf.inta    ),
								        tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]  ,     config.pad_token_id    )  ,     tf.inta    ),
								    ]  ,     axis=-1  ,     )
				if head_mask is None:
								UpperCamelCase_:				Optional[Any]						=    tf.ones((config.encoder_layers, config.encoder_attention_heads)    )
				if decoder_head_mask is None:
								UpperCamelCase_:				str						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
				if cross_attn_head_mask is None:
								UpperCamelCase_:				str						=    tf.ones((config.decoder_layers, config.decoder_attention_heads)    )
				return {
				    "input_ids": input_ids,
				    "decoder_input_ids": decoder_input_ids,
				    "attention_mask": attention_mask,
				    "decoder_attention_mask": decoder_attention_mask,
				    "head_mask": head_mask,
				    "decoder_head_mask": decoder_head_mask,
				    "cross_attn_head_mask": cross_attn_head_mask,
				}

@require_tf
class 							_UpperCamelCase					(       _A  ,							_A  ,							unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    Dict          =			(TFMBartForConditionalGeneration, TFMBartModel) if is_tf_available() else ()
			__UpperCamelCase   :    Optional[Any]          =			(TFMBartForConditionalGeneration,) if is_tf_available() else ()
			__UpperCamelCase   :    str          =			(
			    {
			        """conversational""": TFMBartForConditionalGeneration,
			        """feature-extraction""": TFMBartModel,
			        """summarization""": TFMBartForConditionalGeneration,
			        """text2text-generation""": TFMBartForConditionalGeneration,
			        """translation""": TFMBartForConditionalGeneration,
			    }
			    if is_tf_available()
			    else {}
			)
			__UpperCamelCase   :    int          =			True
			__UpperCamelCase   :    Optional[Any]          =			False
			__UpperCamelCase   :    Any          =			False

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Any    ,						snake_case_			:							int    ,						snake_case_			:							Dict    ,						snake_case_			:							List[str]    ,						snake_case_			:							Optional[int]   ):
							if pipeline_test_casse_name != "FeatureExtractionPipelineTests":
											# Exception encountered when calling layer '...'
											return True

							return False

			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							UpperCamelCase_:				Any						=    TFMBartModelTester(self   )
							UpperCamelCase_:				Optional[int]						=    ConfigTester(self    ,						config_class=snake_case_   )

			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							self.config_tester.run_common_tests()




			def    lowerCAmelCase__						(							self			:							str   ):
							UpperCamelCase_:				str						=    self.model_tester.prepare_config_and_inputs_for_common()
							self.model_tester.check_decoder_model_past_large_inputs(*snake_case_   )




@require_sentencepiece
@require_tokenizers
@require_tf
class 							_UpperCamelCase					(       unittest.TestCase	):






			'''simple docstring'''

			__UpperCamelCase   :    List[str]          =			[
			    """ UN Chief Says There Is No Military Solution in Syria""",
			]
			__UpperCamelCase   :    Any          =			[
			    """Şeful ONU declară că nu există o soluţie militară în Siria""",
			]
			__UpperCamelCase   :    int          =			"""facebook/mbart-large-en-ro"""

			@cached_property
			def    lowerCAmelCase__						(							self			:							Any   ):
							return AutoTokenizer.from_pretrained(self.model_name   )

			@cached_property
			def    lowerCAmelCase__						(							self			:							Optional[int]   ):
							UpperCamelCase_:				int						=    TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name   )
							return model

			def    lowerCAmelCase__						(							self			:							List[str]    ,						**snake_case_			:							Dict   ):
							UpperCamelCase_:				str						=    self.translate_src_text(**snake_case_   )
							self.assertListEqual(self.expected_text    ,						snake_case_   )

			def    lowerCAmelCase__						(							self			:							List[str]    ,						**snake_case_			:							Union[str, Any]   ):
							UpperCamelCase_:				Optional[Any]						=    self.tokenizer(self.src_text    ,						**snake_case_    ,						return_tensors="""tf"""   )
							UpperCamelCase_:				Any						=    self.model.generate(
							    model_inputs.input_ids    ,						attention_mask=model_inputs.attention_mask    ,						num_beams=2   )
							UpperCamelCase_:				Union[str, Any]						=    self.tokenizer.batch_decode(snake_case_    ,						skip_special_tokens=snake_case_   )
							return generated_words




			@slow
			def    lowerCAmelCase__						(							self			:							Union[str, Any]   ):
							self._assert_generated_batch_equal_expected()








from __future__ import annotations

import time

lowerCamelCase_    :			Union[str, Any]    =	list[tuple[int, int]]

lowerCamelCase_    :			str    =	[
    [0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 0, 0, 0],  # 0 are free path whereas 1's are obstacles
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0, 0, 0],
    [1, 0, 1, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1, 0, 0],
]

lowerCamelCase_    :			Dict    =	[[-1, 0], [0, -1], [1, 0], [0, 1]]  # up, left, down, right

class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Dict    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				str						=    pos_x
							UpperCamelCase_:				List[str]						=    pos_y
							UpperCamelCase_:				str						=    (pos_y, pos_x)
							UpperCamelCase_:				Any						=    goal_x
							UpperCamelCase_:				Optional[int]						=    goal_y
							UpperCamelCase_:				Union[str, Any]						=    parent




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							str    ,						snake_case_			:							tuple[int, int]    ,						snake_case_			:							tuple[int, int]   ):
							UpperCamelCase_:				Optional[Any]						=    Node(start[1]    ,						start[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )
							UpperCamelCase_:				List[str]						=    Node(goal[1]    ,						goal[0]    ,						goal[1]    ,						goal[0]    ,						snake_case_   )

							UpperCamelCase_:				Any						=    [self.start]
							UpperCamelCase_:				Dict						=    False

			def    lowerCAmelCase__						(							self			:							str   ):
							while self.node_queue:
											UpperCamelCase_:				int						=    self.node_queue.pop(0   )

											if current_node.pos == self.target.pos:
															UpperCamelCase_:				List[str]						=    True
															return self.retrace_path(snake_case_   )

											UpperCamelCase_:				List[str]						=    self.get_successors(snake_case_   )

											for node in successors:
															self.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.start.pos]
							return None

			def    lowerCAmelCase__						(							self			:							Dict    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				int						=    []
							for action in delta:
											UpperCamelCase_:				Union[str, Any]						=    parent.pos_x + action[1]
											UpperCamelCase_:				str						=    parent.pos_y + action[0]
											if not (0 <= pos_x <= len(grid[0]   ) - 1 and 0 <= pos_y <= len(snake_case_   ) - 1):
															continue

											if grid[pos_y][pos_x] != 0:
															continue

											successors.append(
											    Node(snake_case_    ,						snake_case_    ,						self.target.pos_y    ,						self.target.pos_x    ,						snake_case_   )   )
							return successors




			def    lowerCAmelCase__						(							self			:							int    ,						snake_case_			:							Node | None   ):
							UpperCamelCase_:				int						=    node
							UpperCamelCase_:				Tuple						=    []
							while current_node is not None:
											path.append((current_node.pos_y, current_node.pos_x)   )
											UpperCamelCase_:				List[Any]						=    current_node.parent
							path.reverse()
							return path




class 							_UpperCamelCase					:






			'''simple docstring'''

			def __init__(							self			:							Optional[Any]    ,						snake_case_			:							Optional[Any]    ,						snake_case_			:							Optional[Any]   ):
							UpperCamelCase_:				Tuple						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				Dict						=    BreadthFirstSearch(snake_case_    ,						snake_case_   )
							UpperCamelCase_:				int						=    False

			def    lowerCAmelCase__						(							self			:							int   ):
							while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
											UpperCamelCase_:				List[Any]						=    self.fwd_bfs.node_queue.pop(0   )
											UpperCamelCase_:				Optional[Any]						=    self.bwd_bfs.node_queue.pop(0   )

											if current_bwd_node.pos == current_fwd_node.pos:
															UpperCamelCase_:				List[Any]						=    True
															return self.retrace_bidirectional_path(
															    snake_case_    ,						snake_case_   )

											UpperCamelCase_:				Optional[Any]						=    current_bwd_node
											UpperCamelCase_:				List[Any]						=    current_fwd_node

											UpperCamelCase_:				List[str]						=    {
											    self.fwd_bfs: self.fwd_bfs.get_successors(snake_case_   ),
											    self.bwd_bfs: self.bwd_bfs.get_successors(snake_case_   ),
											}

											for bfs in [self.fwd_bfs, self.bwd_bfs]:
															for node in successors[bfs]:
																			bfs.node_queue.append(snake_case_   )

							if not self.reached:
											return [self.fwd_bfs.start.pos]
							return None




			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							Node    ,						snake_case_			:							Node   ):
							UpperCamelCase_:				List[str]						=    self.fwd_bfs.retrace_path(snake_case_   )
							UpperCamelCase_:				Tuple						=    self.bwd_bfs.retrace_path(snake_case_   )
							bwd_path.pop()
							bwd_path.reverse()
							UpperCamelCase_:				List[str]						=    fwd_path + bwd_path
							return path


if __name__ == "__main__":
							# all coordinates are given in format [y,x]
							import doctest

							doctest.testmod()
							lowerCamelCase_    :			Tuple    =	(0, 0)
							lowerCamelCase_    :			Union[str, Any]    =	(len(grid) - 1, len(grid[0]) - 1)
							for elem in grid:
														print(elem)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Tuple    =	BreadthFirstSearch(init, goal)
							lowerCamelCase_    :			List[str]    =	bfs.search()
							lowerCamelCase_    :			Optional[int]    =	time.time() - start_bfs_time

							print("""Unidirectional BFS computation time : """, bfs_time)

							lowerCamelCase_    :			Optional[int]    =	time.time()
							lowerCamelCase_    :			Optional[int]    =	BidirectionalBreadthFirstSearch(init, goal)
							lowerCamelCase_    :			str    =	bd_bfs.search()
							lowerCamelCase_    :			Tuple    =	time.time() - start_bd_bfs_time

							print("""Bidirectional BFS computation time : """, bd_bfs_time)






